




'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json",
}





class  __lowercase    (	__magic_name__			):
   _a    						=   """gpt_neox_japanese"""







   def __init__(						self   ,				UpperCamelCase=3_2000   ,				UpperCamelCase=2560   ,				UpperCamelCase=32   ,				UpperCamelCase=32   ,				UpperCamelCase=4   ,				UpperCamelCase="gelu"   ,				UpperCamelCase=1.00   ,				UpperCamelCase=1_0000   ,				UpperCamelCase=2048   ,				UpperCamelCase=0.02   ,				UpperCamelCase=1e-5   ,				UpperCamelCase=True   ,				UpperCamelCase=3_1996   ,				UpperCamelCase=3_1999   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.0   ,				**UpperCamelCase   ,				)					->     Tuple:
     super().__init__(bos_token_id=UpperCamelCase   ,				eos_token_id=UpperCamelCase   ,				**UpperCamelCase   )
     __a								=       vocab_size
     __a								=       max_position_embeddings
     __a								=       hidden_size
     __a								=       num_hidden_layers
     __a								=       num_attention_heads
     __a								=       intermediate_multiple_size
     __a								=       hidden_act
     __a								=       rotary_pct
     __a								=       rotary_emb_base
     __a								=       initializer_range
     __a								=       layer_norm_eps
     __a								=       use_cache
     __a								=       attention_dropout
     __a								=       hidden_dropout









'''simple docstring'''


import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict


@pytest.mark.parametrize(
    'files'					,       [
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]					,       )
def     SCREAMING_SNAKE_CASE							(   a_						:				Tuple					,       a_						:				Dict      ):
  __a								=       tmp_path_factory.mktemp('dset_infos_dir'      )
  if "full:README.md" in files:
    with open(dataset_infos_dir / 'README.md'					,       'w'      ) as f:
      f.write('---\ndataset_info:\n  dataset_size: 42\n---'      )
  if "empty:README.md" in files:
    with open(dataset_infos_dir / 'README.md'					,       'w'      ) as f:
      f.write(''      )
    # we want to support dataset_infos.json for backward compatibility
  if "dataset_infos.json" in files:
    with open(dataset_infos_dir / 'dataset_infos.json'					,       'w'      ) as f:
      f.write('{"default": {"dataset_size": 42}}'      )
  __a								=       DatasetInfosDict.from_directory(a_      )
  assert dataset_infos
  assert dataset_infos["default"].dataset_size == 42


@pytest.mark.parametrize(
    'dataset_info'					,       [
        DatasetInfo(),
        DatasetInfo(
            description='foo'					,       features=Features({'a': Value('int32'      )}      )					,       builder_name='builder'					,       config_name='config'					,       version='1.0.0'					,       splits=[{'name': 'train'}]					,       download_size=42					,       ),
    ]					,       )
def     SCREAMING_SNAKE_CASE							(   a_						:				Optional[int]					,       a_						:				DatasetInfo      ):
  __a								=       str(a_      )
  dataset_info.write_to_directory(a_      )
  __a								=       DatasetInfo.from_directory(a_      )
  assert dataset_info == reloaded
  assert os.path.exists(os.path.join(a_					,       'dataset_info.json'      )      )


def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       DatasetInfo(
      description='foo'					,       citation='bar'					,       homepage='https://foo.bar'					,       license='CC0'					,       features=Features({'a': Value('int32'      )}      )					,       post_processed={}					,       supervised_keys=()					,       task_templates=[]					,       builder_name='builder'					,       config_name='config'					,       version='1.0.0'					,       splits=[{'name': 'train', 'num_examples': 42}]					,       download_checksums={}					,       download_size=1337					,       post_processing_size=442					,       dataset_size=1234					,       size_in_bytes=1337 + 442 + 1234					,       )
  __a								=       dataset_info._to_yaml_dict()
  assert sorted(a_      ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML      )
  for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
    assert key in dataset_info_yaml_dict
    assert isinstance(dataset_info_yaml_dict[key]					,       (list, dict, int, str)      )
  __a								=       yaml.safe_dump(a_      )
  __a								=       yaml.safe_load(a_      )
  assert dataset_info_yaml_dict == reloaded


def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       DatasetInfo()
  __a								=       dataset_info._to_yaml_dict()
  assert dataset_info_yaml_dict == {}


@pytest.mark.parametrize(
    'dataset_infos_dict'					,       [
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}      ),
        DatasetInfosDict({'my_config_name': DatasetInfo()}      ),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'					,       features=Features({'a': Value('int32'      )}      )					,       builder_name='builder'					,       config_name='config'					,       version='1.0.0'					,       splits=[{'name': 'train'}]					,       download_size=42					,       )
            }      ),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42      ),
                'v2': DatasetInfo(dataset_size=1337      ),
            }      ),
    ]					,       )
def     SCREAMING_SNAKE_CASE							(   a_						:				List[str]					,       a_						:				DatasetInfosDict      ):
  __a								=       str(a_      )
  dataset_infos_dict.write_to_directory(a_      )
  __a								=       DatasetInfosDict.from_directory(a_      )

  # the config_name of the dataset_infos_dict take over the attribute
  for config_name, dataset_info in dataset_infos_dict.items():
    __a								=       config_name
    # the yaml representation doesn't include fields like description or citation
    # so we just test that we can recover what we can from the yaml
    __a								=       DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()      )
  assert dataset_infos_dict == reloaded

  if dataset_infos_dict:
    assert os.path.exists(os.path.join(a_					,       'README.md'      )      )



